The importance of graphite is increasing with the global shift toward carbon neutrality and clean energy transitions. As a critical mineral for battery anodes, graphite demand is projected to grow fourfold by 2030 compared to 2023 levels, with overall demand doubling during the same period. However, graphite is classified as a high-risk mineral due to its heavily concentrated supply chain, which poses significant geopolitical risks. The globalization of manufacturing and trade has further exacerbated supply chain vulnerabilities by centralizing production in low-cost regions, deepening dependence on specific countries. This letter provides a comprehensive overview of the graphite supply chain and its associated challenges while offering strategic insights for stabilization and diversification.

This study proposes a 3D global localization method that implements mobile LiDAR mapping and point cloud registration to recognize the locations of objects in an underground mine. An initial global point cloud map was built for an entire underground mine area using mobile LiDAR; a local LiDAR scan (local point cloud) was generated at the point where underground positioning was required. We calculated fast point feature histogram (FPFH) descriptors for the global and local point clouds to extract point features. The match areas between the global and the local point clouds were searched and aligned using random sample consensus (RANSAC) and iterative closest point (ICP) registration. The object’s location on the global coordinate system was measured using the LiDAR sensor trajectory. Field experiments were performed at the Gwan-in underground mine using three mobile LiDAR systems. The local point cloud dataset formed for the six areas of the underground mine precisely matched the global point cloud, with a low average error of approximately 0.13 m, regardless of the type of mobile LiDAR system used. In addition, the LiDAR senor trajectory was aligned on the global coordinate system to confirm the change in the dynamic object’s position over time.

Airborne dust is a byproduct of natural and artificial occurrences, including high winds in arid regions and human activities, and it affects most of the world's population. Watering is the most general practice for reducing airborne dust by wetting the surface of the dust source to agglomerate dust particles via the capillary effect, increasing the aerodynamic diameter of (ultra)fine particles and reducing dust emission. However, the short-term effectiveness due to fast water evaporation, requiring frequent watering, is a major disadvantage. Herein, we utilized biocompatible liquid polymers as additives in water to prolong moist conditions of dust sources due to their liquid state. After the water evaporated, the liquid polymers maintained moisture on the dust sources, resulting in significantly reduced (ultra)fine particle emissions and extended effectiveness compared to conventional water treatment. Interestingly, we observed greater dust suppressive effectiveness with liquid amphiphilic polymer than liquid hydrophilic polymer because of the synergistic effect of the liquid state and amphiphilic property of the polymer. Translating lab-scale experiments to pilot-scale field-testing confirmed the potential for utilizing biocompatible liquid amphiphilic polymers to advance airborne dust suppression technology.

This study was conducted to produce high-grade graphite concentrate by applying the traditional beneficiation process to two types of low-grade graphite ores in Bonghwa and Gapyeong regions of Korea. The physicochemical properties of graphite ores

The development of artificial intelligence (AI) technology following the Fourth Industrial Revolution has driven innovation across various industries, significantly altering economic and social structures. The advancement of AI models has heightened the demand for high-performance semiconductors capable of extensive computation, making them critical components in data centers, autonomous vehicles, 5G/6G telecommunications, cloud computing, and other sectors. Consequently, the semiconductor industry's rapid growth has emphasized the necessity of securing stable supply chains for essential minerals and raw materials required for semiconductor manufacturing. However, recent supply chain risks have intensified due to factors such as China's restrictions on critical mineral exports, disruptions caused by the Russia-Ukraine conflict, tightening ESG regulations, and global fluctuations in raw material prices. This study identifies key minerals essential to the semiconductor industry and analyzes the associated supply chain risks, providing foundational data to ensure the stable operation of the semiconductor sector, a critical national industry.

In hard rock mine operations, the gravitational segregation of ore particles within a stockpile creates a high variation of particle size. This issue hampers productivity in the comminution process and has a direct effect on the daily output of produced minerals from the mine. Dozer operation can be facilitated to mix the stockpile materials for maintaining a more homogeneous particle size distribution, however this can lead to potentially hazardous situations for the machine and operator due to the dynamic nature of the stockpile. The use of autonomous bulldozers is a new trend for mine equipment operation, improving the safety of dozer operators working in the stockpile by removing them from the machine altogether. This paper describes path-planning strategy for autonomous dozers to operate over a stockpile within a predefined safe region. For this purpose, a stockpile model is developed, where level and particle size properties are used to describe the stockpile in terms of sectors. This information is then used to command the operation of an autonomous dozer over the stockpile based on current SAG mill feed state. Simulation results predict acceptable behavior, provided that sensor information provides enough accuracy to specify safety and performance constrains.